memstore 0.2.3.1

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MemStore

MemStore is a lightweight in-memory database written in Python. It supports key-value storage with integer IDs, single-field indexing, and filtering by field values. It uses dictionaries for data storage and retrieval.

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Installation

Since MemStore is a single-class implementation, you can simply include it in your project. No external package installation is required. Alternatively, if packaged:

pip install memstore

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Usage Examples

1. Initialize the Database

Create a database with optional indexes:

from memstore import MemStore

# Initialize with indexes on 'name' and 'age'
db = MemStore(indexes=['name', 'age'])

2. Insert Records

Add a single record and get its ID:

# Insert a single record
record_id = db.add({'name': 'Alice', 'age': 25, 'city': 'New York'})
print(f"Inserted record with ID: {record_id}")  # Output: Inserted record with ID: 0

3. Query Records

Retrieve records by ID or filter by field values:

# Get by ID
record = db.get(0)
print(record)  # Output: {'name': 'Alice', 'age': 25, 'city': 'New York'}

# Filter by indexed field
alice_records = db.filter({'name': 'Alice'})
print(alice_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

# Filter by non-indexed field
ny_records = db.filter({'city': 'New York'})
print(ny_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

# Filter with multiple conditions (mixed indexed and non-indexed)
alice_25_records = db.filter({'name': 'Alice', 'age': 25})
print(alice_25_records)  # Output: [(0, {'name': 'Alice', 'age': 25, 'city': 'New York'})]

4. List All Records

Retrieve all records in the database:

db.add({'name': 'Bob', 'age': 30, 'city': 'Boston'})
all_records = db.all()
for record_id, record in all_records:
    print(f"ID {record_id}: {record}")
# Output:
# ID 0: {'name': 'Alice', 'age': 25, 'city': 'New York'}
# ID 1: {'name': 'Bob', 'age': 30, 'city': 'Boston'}

5. Delete Records

Remove a record by ID:

success = db.delete(0)
print(f"Delete successful: {success}")  # Output: Delete successful: True
print(db.all())  # Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'})]

6. Manage Indexes

Add or remove indexes dynamically:

# Add a new index
db.add_index('city')
print(db.filter({'city': 'Boston'}))  # Output: [(1, {'name': 'Bob', 'age': 30, 'city': 'Boston'})]

# Drop an index
db.drop_index('name')
print('name' in db._indexes)  # Output: False

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Notes

• Data Structure: Records are stored as dictionaries with integer IDs assigned sequentially.
• Indexes: Only single-field indexes are supported (e.g., 'name'). Composite indexes are not available.
• Filtering: The filter method retrieves records matching all specified field-value pairs, using indexes when available for efficiency. It works with both indexed and non-indexed fields.
• Limitations: No field validation or update methods are provided. Deletion and retrieval are ID-based or filter-based only.
• Dependencies: Uses only Python standard library modules (collections, functools, itertools, operator, typing).